Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems
Beam tracking is an essential functionality of millimeter wave (mmWave, 30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates by performing antenna sweeping at both base station (BS) and user equipment (UE) sides using the Synchronization Signal Blocks (SSB). The optimal fr...
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| creator | Shurakov, Alexander Ershova, Margarita Khakimov, Abdukodir Prikhodko, Anatoliy Mokrov, Evgeny Begishev, Vyacheslav Chulkova, Galina Koucheryavy, Yevgeni Gol'tsman, Gregory |
| description | Beam tracking is an essential functionality of millimeter wave (mmWave,
30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates
by performing antenna sweeping at both base station (BS) and user equipment
(UE) sides using the Synchronization Signal Blocks (SSB). The optimal frequency
of beam tracking events is not specified by 3GPP standards and heavily depends
on the micromobility properties of the applications currently utilized by the
user. In absence of explicit signalling for the type of application at the air
interface, in this paper, we propose a way to remotely detect it at the BS side
based on the received signal strength pattern. To this aim, we first perform a
multi-stage measurement campaign at 156 GHz, belonging to the sub-THz band, to
obtain the received signal strength traces of popular smartphone applications.
Then, we proceed applying conventional statistical Mann-Whitney tests and
various machine learning (ML) based classification techniques to discriminate
applications remotely. Our results show that Mann-Whitney test can be used to
differentiate between fast and slow application classes with a confidence of
0.95 inducing class detection delay on the order of 1 s after application
initialization. With the same time budget, random forest classifiers can
differentiate between applications with fast and slow micromobility with 80%
accuracy using received signal strength metric only. The accuracy of detecting
a specific application however is lower, reaching 60%. By utilizing the
proposed technique one can estimate the optimal values of the beam tracking
intervals without adding additional signalling to the air interface. |
| doi_str_mv | 10.48550/arxiv.2410.18637 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2410_18637</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2410_18637</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2410_186373</originalsourceid><addsrcrecordid>eNqFzrsOgjAYBeAuDkZ9ACf_F-AmF1m9gquSODiQij-mkdKmrUR8eoW4M52ckzN8hMw91w7iMHQdqt6ssZfBb_DiyF-NyfWEXBiEHRosDBM1iBLWUlasoF3VUAoFRy6VaPAOG6QcMkWLJ6sfwGrg_EIbdPTrZmXpB8LEiRI4t9og11MyKmmlcfbPCVkc9tk2tXpGLhXjVLV5x8l7jj_8-AJ63z9r</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems</title><source>arXiv.org</source><creator>Shurakov, Alexander ; Ershova, Margarita ; Khakimov, Abdukodir ; Prikhodko, Anatoliy ; Mokrov, Evgeny ; Begishev, Vyacheslav ; Chulkova, Galina ; Koucheryavy, Yevgeni ; Gol'tsman, Gregory</creator><creatorcontrib>Shurakov, Alexander ; Ershova, Margarita ; Khakimov, Abdukodir ; Prikhodko, Anatoliy ; Mokrov, Evgeny ; Begishev, Vyacheslav ; Chulkova, Galina ; Koucheryavy, Yevgeni ; Gol'tsman, Gregory</creatorcontrib><description>Beam tracking is an essential functionality of millimeter wave (mmWave,
30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates
by performing antenna sweeping at both base station (BS) and user equipment
(UE) sides using the Synchronization Signal Blocks (SSB). The optimal frequency
of beam tracking events is not specified by 3GPP standards and heavily depends
on the micromobility properties of the applications currently utilized by the
user. In absence of explicit signalling for the type of application at the air
interface, in this paper, we propose a way to remotely detect it at the BS side
based on the received signal strength pattern. To this aim, we first perform a
multi-stage measurement campaign at 156 GHz, belonging to the sub-THz band, to
obtain the received signal strength traces of popular smartphone applications.
Then, we proceed applying conventional statistical Mann-Whitney tests and
various machine learning (ML) based classification techniques to discriminate
applications remotely. Our results show that Mann-Whitney test can be used to
differentiate between fast and slow application classes with a confidence of
0.95 inducing class detection delay on the order of 1 s after application
initialization. With the same time budget, random forest classifiers can
differentiate between applications with fast and slow micromobility with 80%
accuracy using received signal strength metric only. The accuracy of detecting
a specific application however is lower, reaching 60%. By utilizing the
proposed technique one can estimate the optimal values of the beam tracking
intervals without adding additional signalling to the air interface.</description><identifier>DOI: 10.48550/arxiv.2410.18637</identifier><language>eng</language><subject>Computer Science - Learning ; Physics - Instrumentation and Detectors</subject><creationdate>2024-10</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.18637$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.18637$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Shurakov, Alexander</creatorcontrib><creatorcontrib>Ershova, Margarita</creatorcontrib><creatorcontrib>Khakimov, Abdukodir</creatorcontrib><creatorcontrib>Prikhodko, Anatoliy</creatorcontrib><creatorcontrib>Mokrov, Evgeny</creatorcontrib><creatorcontrib>Begishev, Vyacheslav</creatorcontrib><creatorcontrib>Chulkova, Galina</creatorcontrib><creatorcontrib>Koucheryavy, Yevgeni</creatorcontrib><creatorcontrib>Gol'tsman, Gregory</creatorcontrib><title>Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems</title><description>Beam tracking is an essential functionality of millimeter wave (mmWave,
30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates
by performing antenna sweeping at both base station (BS) and user equipment
(UE) sides using the Synchronization Signal Blocks (SSB). The optimal frequency
of beam tracking events is not specified by 3GPP standards and heavily depends
on the micromobility properties of the applications currently utilized by the
user. In absence of explicit signalling for the type of application at the air
interface, in this paper, we propose a way to remotely detect it at the BS side
based on the received signal strength pattern. To this aim, we first perform a
multi-stage measurement campaign at 156 GHz, belonging to the sub-THz band, to
obtain the received signal strength traces of popular smartphone applications.
Then, we proceed applying conventional statistical Mann-Whitney tests and
various machine learning (ML) based classification techniques to discriminate
applications remotely. Our results show that Mann-Whitney test can be used to
differentiate between fast and slow application classes with a confidence of
0.95 inducing class detection delay on the order of 1 s after application
initialization. With the same time budget, random forest classifiers can
differentiate between applications with fast and slow micromobility with 80%
accuracy using received signal strength metric only. The accuracy of detecting
a specific application however is lower, reaching 60%. By utilizing the
proposed technique one can estimate the optimal values of the beam tracking
intervals without adding additional signalling to the air interface.</description><subject>Computer Science - Learning</subject><subject>Physics - Instrumentation and Detectors</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFzrsOgjAYBeAuDkZ9ACf_F-AmF1m9gquSODiQij-mkdKmrUR8eoW4M52ckzN8hMw91w7iMHQdqt6ssZfBb_DiyF-NyfWEXBiEHRosDBM1iBLWUlasoF3VUAoFRy6VaPAOG6QcMkWLJ6sfwGrg_EIbdPTrZmXpB8LEiRI4t9og11MyKmmlcfbPCVkc9tk2tXpGLhXjVLV5x8l7jj_8-AJ63z9r</recordid><startdate>20241024</startdate><enddate>20241024</enddate><creator>Shurakov, Alexander</creator><creator>Ershova, Margarita</creator><creator>Khakimov, Abdukodir</creator><creator>Prikhodko, Anatoliy</creator><creator>Mokrov, Evgeny</creator><creator>Begishev, Vyacheslav</creator><creator>Chulkova, Galina</creator><creator>Koucheryavy, Yevgeni</creator><creator>Gol'tsman, Gregory</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241024</creationdate><title>Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems</title><author>Shurakov, Alexander ; Ershova, Margarita ; Khakimov, Abdukodir ; Prikhodko, Anatoliy ; Mokrov, Evgeny ; Begishev, Vyacheslav ; Chulkova, Galina ; Koucheryavy, Yevgeni ; Gol'tsman, Gregory</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_186373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Learning</topic><topic>Physics - Instrumentation and Detectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Shurakov, Alexander</creatorcontrib><creatorcontrib>Ershova, Margarita</creatorcontrib><creatorcontrib>Khakimov, Abdukodir</creatorcontrib><creatorcontrib>Prikhodko, Anatoliy</creatorcontrib><creatorcontrib>Mokrov, Evgeny</creatorcontrib><creatorcontrib>Begishev, Vyacheslav</creatorcontrib><creatorcontrib>Chulkova, Galina</creatorcontrib><creatorcontrib>Koucheryavy, Yevgeni</creatorcontrib><creatorcontrib>Gol'tsman, Gregory</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shurakov, Alexander</au><au>Ershova, Margarita</au><au>Khakimov, Abdukodir</au><au>Prikhodko, Anatoliy</au><au>Mokrov, Evgeny</au><au>Begishev, Vyacheslav</au><au>Chulkova, Galina</au><au>Koucheryavy, Yevgeni</au><au>Gol'tsman, Gregory</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems</atitle><date>2024-10-24</date><risdate>2024</risdate><abstract>Beam tracking is an essential functionality of millimeter wave (mmWave,
30-100 GHz) and sub-terahertz (sub-THz, 100-300 GHz) 5G/6G systems. It operates
by performing antenna sweeping at both base station (BS) and user equipment
(UE) sides using the Synchronization Signal Blocks (SSB). The optimal frequency
of beam tracking events is not specified by 3GPP standards and heavily depends
on the micromobility properties of the applications currently utilized by the
user. In absence of explicit signalling for the type of application at the air
interface, in this paper, we propose a way to remotely detect it at the BS side
based on the received signal strength pattern. To this aim, we first perform a
multi-stage measurement campaign at 156 GHz, belonging to the sub-THz band, to
obtain the received signal strength traces of popular smartphone applications.
Then, we proceed applying conventional statistical Mann-Whitney tests and
various machine learning (ML) based classification techniques to discriminate
applications remotely. Our results show that Mann-Whitney test can be used to
differentiate between fast and slow application classes with a confidence of
0.95 inducing class detection delay on the order of 1 s after application
initialization. With the same time budget, random forest classifiers can
differentiate between applications with fast and slow micromobility with 80%
accuracy using received signal strength metric only. The accuracy of detecting
a specific application however is lower, reaching 60%. By utilizing the
proposed technique one can estimate the optimal values of the beam tracking
intervals without adding additional signalling to the air interface.</abstract><doi>10.48550/arxiv.2410.18637</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Physics - Instrumentation and Detectors |
| title | Remote Detection of Applications for Improved Beam Tracking in mmWave/sub-THz 5G/6G Systems |
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